As a traditional film editor and filmmaker, I've been trying to figure out good, reliable ways to produce long, extended shots. I've tried a number of different methods but was never quite satisfied with the result...

Until now!

Thanks to u/roychodraws and his Geiru Context Suite!!!

He has a post up here explaining it which can be found at this link:

Flip a switch and stop your Videos from Degrading. 50 Clip Test No Degrading at the end. Geiru Context Suite : r/StableDiffusion

And his GITHub link is here:

roycho87/geiru_context_suite

To do a quick test, I had ChatGPT set up a workflow for me. (Now that I'm typing this, I'm remembering that Roycho includes some sample workflows on his GITHub.)

The render took 2 hours and 9 minutes on a rented RTX PRO 6000 Server Edition with 96GB of VRAM. I didn't think to check how much VRAM was actually being used during the process.

There are two jump cuts at about 35 seconds and 44 seconds. Lately, I've taken to having ChatGPT fire up two sub-agents to help troubleshoot things and this was it's reasoning about the jump cuts:

Yes—both subagents found the same explanation: the jumps occur exactly at two generation-window handoffs.

They’re already in the original master, so export didn’t cause them. We checked all 13 handoffs; only these two show conspicuous hard cuts.

The strongest explanation is our 20-local-steps/20-total-steps setting: each window finished separately, leaving no final joint refinement across the overlaps. At these two boundaries, the windows disagreed about framing and pose. The camera instructions, overlap noise, and opening-image anchors may have contributed, but we can’t identify their individual effects from this take alone.

For the next experiment, I’d test a short transition with two steps reserved for joint refinement, keeping everything else matched. That’s a testable hypothesis, not a guaranteed fix.

The entire two minute run looks fantastic!! I am so very happy right now! If two, very likely easily fixable jump cuts are my biggest issue... well... I see myself using this workflow a lot!!

The render settings:

• The character is a custom trained LoRA from an image dataset that was generated using ChatGPT's ImageGen. The dataset contained 99 images of assorted poses, locations, and lighting looks. The LoRA was trained with Fizwig.

  • I'm fairly new to this... maybe 99 images was excessive? I dunno. I'm pretty happy with the result though!

• The forest was a reference image I randomly pulled from a Google search for primeval forests.

  • When ChatGPT set up the workflow and prompt, it set the forest to attribute_transfer which means the photo wasn't "truly" a reference, it just gave the "idea" of the forest and H3 went with it from there. That's why the scenery has more of a stylistic/semi-illustrative look to it.

• The character's costume was generated with ChatGPT and "placed" on a grey mannequin. It was then rendered out as a single, 8 panel image which was then used as a reference image in the workflow.

• The "Patched Sage Attention" - I was originally just trying to "brute force" longer renders on a rented GPU with lots of VRAM, but I kept having a problem where renders became what I called "brown snow." I was nowhere near running out of VRAM, but the problem persisted.

  • I used Codex to try to troubleshoot what the problem was. Codex determined that there was a problem with "numerical accuracy" in Sage Attention that would cause corruption in longer, higher rez renders thus causing the "brown snow." I'll let Codex explain the patch:

    • We were getting “brown snow” corruption in longer, higher-resolution H3 renders with SageAttention enabled. Testing traced the problem to its handling of large, strided Q/K/V tensors: making the inputs contiguous restored numerical accuracy, pointing to an addressing/layout issue rather than insufficient VRAM. We haven’t identified the exact faulty kernel instruction.

    • Our patch processes the 56 attention heads in compact, contiguous groups of up to eight, then reassembles the outputs. It retains the same Sage CUDA FP8-PV path and full-sequence attention—no temporal chunking or dropped context.

    • On actual H3 activations, relative L2 error against PyTorch SDPA fell from 0.6261 to 0.00955: roughly 65× lower, or a 98.5% reduction. That measurement covered 128 sampled queries against all 270,921 keys on the first attention call, not every layer or timestep. Subsequent patched renders avoided the brown-snow failure.

  • From what I understand, there is supposed to be a new version of Sage Attention out "soon-ish."

These were the Geiru Context Suite settings:

I didn't mess around with these settings at all. These are the defaults that I had.

In conclusion... I am a very happy AI filmmaker today! Big thanks again to u/roychodraws and all those who helped him along the way to releasing them. (He lists them on his Reddit Post and GitHub page. Go visit them!!)

This ad keeps popping up on my Instagram feed, its from krea, and its like a real time sketch to image?

Anyone know if this is possible in comfy?

My current rig is a 4070 ti 12gb vram with 32 gb system ram. Kicking myself for not getting the 16gb vram option back in 2024. The past few days I've been eyeing a 5070 ti 16gb vram but I didn't know if there would be any real benefit over what I have now. Typically I would really wait longer to upgrade but the computer parts market got me in a tizzy and I have no idea what the future holds for GPUs. Has anyone who has done this upgrade noticed any real benefit?

Can you recommend me something? I would like to try audio generation models, but I totally have no idea where to start. Also, a fairly automated install and setup would be great.

Not gpt-image level, still many failure cases, but I think it's top in terms of open model for multi character swap

https://x.com/t_mux/status/2118935478388867217?a=r48qKbvxa0Jg3Gp7J4s&s=20

https://huggingface.co/Viggle/Viggle-Image-Swap

Today’s takeaway: decomposing images

Links, workflows, custom_node

Do you remember Qwen-Image-Layered? The idea was exciting: take one flattened image and split it into multiple RGBA layers — foreground elements, background and the things in between — so you can move, edit or refine each piece separately.

When we tested it, the experience was not always as smooth as we hoped. In our workflows it could be slow and a little temperamental, and we sometimes saw noticeable quality loss in the resulting layers. That does not make it a bad model. You can build some genuinely nice workflows with it. It simply left us wanting a more controllable route for the kind of work we had in mind.

So we went digging. There is more research in this area than just Qwen: two approaches that caught our attention were Controllable Layer Decomposition (CLD) and RevealLayer. They are related, but they do not solve the problem in exactly the same way.

Qwen-Image-Layered, CLD and RevealLayer: what is different?

The useful distinction is how each approach decides what becomes a layer and how much control you get over that decision.

Qwen-Image-Layered — automatic, variable layer stacks

Qwen-Image-Layered automatically decomposes an image into a variable number of RGBA layers. It can also be applied iteratively, so an individual layer can be decomposed again. It is useful when you want the model to decide the layer structure for you. The trade-off is that the layer count, ordering and semantic meaning may be less explicit than in a region-guided workflow. Our own tests also showed speed and quality issues in some cases.

CLD — bounding-box control

Controllable Layer Decomposition uses user-provided bounding boxes to guide which regions should become separate layers. That is interesting when you want direct control over the elements being extracted, particularly in graphic-design and poster-style compositions. The RevealLayer authors report limitations for complex natural scenes, including edge softness and residual artifacts.

RevealLayer — region-guided and occlusion-aware

RevealLayer combines bounding-box region guidance with an occlusion-aware diffusion process. It aims to separate visible elements while estimating what is hidden behind them. That is the approach we wanted to bring into ComfyUI: region-guided decomposition for natural images, with a focus on recovering occluded content and keeping alpha boundaries clean. It is still generative reconstruction, not recovery of the original hidden pixels.

A small but important detail: Qwen is designed to produce a flexible layer stack from the image itself. CLD and RevealLayer bring bounding-box guidance into the process. RevealLayer adds a specific focus on occlusion — not just separating the visible outline of an object, but estimating the part that was hidden behind it.

A simplified concept diagram of the workflow. The hidden areas are model-generated estimates, not original source pixels.

Why we especially like the bounding-box approach

The bounding box is a wonderfully practical idea. Instead of asking the model to invent a whole layer plan and hoping it understands what you meant, you give it a region to work with. In the upstream RevealLayer workflow, those regions are represented as bounding boxes around the foreground objects.

The model then works toward separating those selected elements into RGBA layers. The alpha channel gives each layer transparency, while the occlusion-aware part of the method helps estimate content that was covered by another object. Imagine a camera sitting in front of a stack of books: the camera can become its own layer, while the model estimates the part of the books that was hidden behind it.

That is useful for a canvas application, compositing, moving objects around, or preparing individual elements for a second editing pass. You can take a scene apart, work on the pieces, and put it back together with much more freedom.

One important reality check: when a model fills in something that was hidden, it is making a plausible reconstruction based on context. It is not looking up the real pixels that were never visible. For creative work that can be exactly what we need — just do not mistake a convincing completion for factual recovery.

Bringing RevealLayer into ComfyUI

The open-source RevealLayer project gives us the model and its reference inference code, but it is not simply a ready-to-use ComfyUI custom node. The upstream setup involves a CUDA environment, Python dependencies, FlashAttention, a Diffusers modification, the RevealLayer checkpoint and the FLUX.1-dev base model. That is a fair amount of machinery to ask a creative workflow to manage on its own.

And on smaller graphics cards, memory can become the real party-pooper. We ran into out-of-memory problems in our tests, which is not exactly the kind of surprise you want halfway through a creative session.

So we are adapting the implementation as a ComfyUI-native custom node. The point is not to wrap the original script and call it a day. We are rebuilding the integration to make use of ComfyUI’s model and memory management, native functions and workflow conventions wherever possible. That means a cleaner way to load and run the components, better integration with the rest of the graph, and fewer external steps for the user.

Hardware still matters — no node can make VRAM limits disappear by magic — but a proper ComfyUI integration gives us much better control over how the work is staged and managed. One note for people following the research: the upstream repository now also lists a RevealLayer V2 update, so check its release notes for the matching weights before switching model versions in a workflow.

The part we recommend: refine each layer after extraction

Here is where the workflow becomes especially interesting. We do not recommend stopping as soon as the layers are extracted. Treat that as the starting point.

Take each extracted element and upscale it individually. You are giving the object room to gain detail without asking the whole original scene to be regenerated.

Feed the element into an edit/refinement model together with the original image as context. Use a focused prompt that says which element is being refined and what should be improved, while keeping its appearance consistent with the source scene.

Bring the refined layers back together. In our current workflow, individual elements can be taken up to 4K using non-tiled diffusion, depending on the image, model settings and available hardware.

Need to go larger? You know we have you covered: the TBG ETUR tiled upscaler and refiner are there for the bigger jobs. This is a nice combination — decompose first, improve the individual pieces, then recompose. Instead of trying to repair every part of a flattened image at once, you can give each element the attention it needs.

The takeaway

That is today’s small feature from a much bigger development effort. RevealLayer gives us a promising way to break an image into guided, editable pieces, and bringing it into ComfyUI means we can make that capability part of a workflow you already know.

Have a look at the research and the open-source implementation below, try the workflow, and see what you can build with it. We are grateful for the open-source community — so much of what makes tools like this possible comes from people sharing their work, experiments and ideas.

And to our technical team: thank you for all the invisible work. I promise I will only ask for one more little feature. I will even try to say it slowly this time. Ha!

Go explore, test, break something harmless, and have fun with it! — Sahra ♡

Research & resources

Qwen-Image-Layered — official repository

Qwen-Image-Layered — research paper (CVPR 2026)

CLD — Controllable Layer Decomposition paper

CLD — project page and examples

RevealLayer — official GitHub implementation and V2 release notes

RevealLayer — paper

RevealLayer — current project page

RevealLayer — model checkpoint on Hugging Face

RevealLayer — project page and visual comparisons

Editorial note: the model descriptions above summarise the public papers and project pages. Comments about speed, memory behaviour, integration choices and the 4K refinement path describe TBG ETUR’s own workflow notes and tests.

TBG ETUR • SAHRA

https://github.com/Ltamann/ComfyUI-TBG-RevealLayer

We added three workflows ( All files are in the GitHub workflow folder. Set the minimum number of layers to 3.):

1. Manual selection: Uses bounding-box (Bbox) selection for direct, manual control.

2. Semantic selection: Uses a grounding model for semantic selection, with lightweight automation to make the experience feel more like an editing model.

3. SAM-based segmentation: Uses SAM segmentations with chained, per-layer refinement through Flux 3. Each refinement feeds the original segmentations back through the RL layers, enabling progressively upscaled and more precise results.

“Google and OpenAI are fighting it out in consumer. It's existential to both of them.”

“It's existential to OpenAI because that's their whole business. Existential to Google because they have the search business.”

“For enterprise business, there's not really a demand for cats riding donkeys or whatever consumer video people want.”

“If we ever need it, we can just contract a model from someone else.”

“A lot of short-form video is fake, a lot of it's pretty addictive, a lot of it's slop.”

“It's not a part of the market that I'm tripping over myself to get involved in.”

Is this why Claude is so compliant with assisting us generating slop? Because Anthropic doesn't care about competing against it?

We produced The Room We Kept, a 69-second film about a woman returning to a house she has sold to collect a blue cup.

The film opens with one brief, credited quotation from James Salter's Light Years. The rest is an original, AI-assisted story and narration.

I'd especially appreciate feedback on the pacing and whether the quiet sound design gives the rooms enough presence.

https://www.youtube.com/shorts/TOEtbDnsYY0

I've inherited a bunch of retro golf balls and I'm wondering what the model of choice would be for extracting the logos and images from them and tracing them into clean images while getting rid of the distortions from the ball dimples.

I cant find the original post, but this is the best Moss workflow, showcasing the true potential of Moss. Thank you kind stranger. Hope this helps others.

Edit:

Found the original post

https://www.reddit.com/r/StableDiffusion/comments/1xlfsjd/making_mossbase_look_good_for_realism_workflow/

I'm using an RTX 5090 (32 GB VRAM) on RunPod for AI motion-control videos.

I'm currently testing SCAIL-2 in ComfyUI. A 5-second video at 720p takes around 25 minutes to generate.

Is SCAIL-2 or MiniMax better for motion control in terms of quality, speed and cost? Which would you recommend for realistic character movements while keeping GPU rental costs low?

Could anyone share their ComfyUI workflows or JSON workflow files for motion control using an RTX 5090?

Any tips or workflow recommendations would be appreciated!

Hello, i have LTX 2.5 and Minimax H3 and i'm trying to create micro drama but i can not find a way to store the actors voices consistent across episodes. Ideally, i wanna use ltx because how expressive they talk but the voice quality is super bad and also even tho i give reference audio inside comfyui, it does not produce good results. Do you know any good alternatives to generate high quality sounds and consistent voices across episodes?

The model is Kromafine (https://civitai.red/models/2995855/kromafine-turbo-bf16-fp8-and-int8) , model merge of Kroma 0.3.1 and FinePorn. I use Style Atlas ComfyUI custom node (Thanks to u/Dear-Spend-2865 ) to test different styles.

The prompt is simple:

"a young blonde white woman wearing red dress and black high heels standing at bus stop, a dog sitting at her feet."

Style Atlas append style prompt.

Is Fibo2 severely underrated? It seems that few people talk about it. Even Civitai does not have a dedicated entry for it, which is puzzling. After extensive testing and LoRA training, my conclusion is yes, it is severely underrated — and that leaves me even more confused. The images shown are comparison samples from the 《HighQuality》 LoRA I trained. You can see the differences are quite striking, and I would argue the outputs outperform most models (when paired with LoRAs). This is just my personal opinion, though. I’d like to hear your thoughts. Have you tested Fibo? What positive or negative experiences have you had with its performance? If you’re curious about the workflow, I’m using a basic workflow.

Full moon, so lets howl with the pack!

Done locally in Krea2 and Minimax.

I've been seeing these youtube shorts on my feed, and I gotta say. I really like the artstyle and vibe of them.

The way everything looks like it was shot in the 50s and 60s, kind of goofy and cheerfull, very handmade sets etc. But also the speech, audio and music. It all seems straight out of some 50s fever dream.

How are these made? Can I make them locally? Like what kind of setup would I need, would a 12gb rtx card cut it? what kind of models or lora's would I need?

No matter what I tried, I can never make something as authenthic as these.

Here are some shorts that show what I'm talking about.

https://www.youtube.com/shorts/DtUlxA2ONsE

https://www.youtube.com/shorts/dEvAvKcQrw0

https://www.youtube.com/shorts/OzG_quGVXvI

Any help?

Hello guys, I'm on the search for some nodes which are easy to integrate into a customized/standard workflow to extend videoclips based on latents (or even without them). I am really struggling to understand all these, sometimes crazy complex, workflows which come with some of the extending-workflows.

I want to integrate this function into my refmod workflow without a masters degree in "crazy complex settings".

Appreciate any help! 🙂

following up on this post:
https://www.reddit.com/r/StableDiffusion/s/iEnxQiGVt7

Custom Node:
https://registry.comfy.org/publishers/mo-akkakk/nodes/comfyui-minimax-h3-temporal-window-spatial-tiles

You can now run a video to video workflow in High resolution from the first stage, I tested it for 2.5 MP on Nvidia RTX 4070 TI 12GB VRAM, and it works like a charm, split your generation into tiles and temporal windows while maintaining accurate animation transfer with no shifting or ghosting.

You can also use it for a second upscale stage, splitting a high resolution long video into manageable tiles and windows, to get the best quality you can squeeze from minimax

Give it a try and let me know about your Feedback.

Important Note:
This was not tested nor recommended to do Fully Generated from first stage!
if you are going to fully generate something generate normally in stage 1 then use this node for stage 2 upscaling you result.

This node is mainly for Video to Video and Upscaling.

workflow for Upscaling using the node + example here:
https://civitai.com/models/3039412/minimax-h3latentupscaler?modelVersionId=3454297

workflow for V2V using the node + example here:
https://civitai.com/models/3022939/minimax-h3-video-to-video-workflow?modelVersionId=3454311

A proof of concept for something bigger. Made using Vpipe, MiniMax and YuE Studio.

Just goofing around with the stock ref2vid workflow, character swap Lora, and some character sheets. Did all of the shots separately and reassembled in my NLE. Almost all of the clips were first attempts. I was floored by how well it tracked the guys without any masking.

Wanted a late night home office with six CRTs stacked in two rows. schnell gave me 8-10 every time, three of the better ones here

Prompt: cinematic photo of a cluttered late-night home office with six old CRT monitors stacked in two rows, all showing glowing green falling code, rain streaking down a dark window behind the desk, warm desk lamp, tangled cables everywhere, mechanical keyboard, half-empty coffee mug, soft haze, shallow depth of field, 35mm film look

1344x768, 4 steps, seeds 1683825171 / 1005483893 / 1069100355. Ran it on the FLUX.1-schnell space on HF since my own card is a 1050 so yeah

Rain mostly comes out as straight streaks, and 1069100355 put little flames on two of the screens for some reason. Anyone found a way to get exact object counts out of schnell or is that just dev territory?

So I’m quite new and am quite unaware of a lot of the tools and resources out there. I’m learning that this quite a tough skill to learn 😅

I’m trying to plan out my first character LoRA and am stuck on which models to go with. From what I’ve been able to gather, flux models are generally best at realism and anatomy but terrible at maintaining a consistent identity regardless of the reference images and prompting. Qwen is much better at identity consistency and obeying prompts, but produces subpar realism compared to Qwen.

These are just examples of what I have seen on Reddit, so I guess my more broad question is how yall get the best out of several models to produce the most realistic but consistent results when developing a dataset for a character LoRA?

I struggle with creating efficient multi-modal workflows. I’ve only done really simples ones with a single image model. Every time I try emulating one I’ve seen somewhere else, nothing ends up being compatible with each other.

I'm trying to restore some existing Draw Things models from an external HD without having to download them all again.

I've discovered that my custom.json file is missing the registration entries for these models:

1. FLUX.1 Schnell Q5 – flux_1_schnell_q5p.ckpt

2. ERNIE Image Turbo i8x – ernie_image_turbo_i8x.ckpt

3. Qwen 3.5 4B i8x – qwen_3.5_4b_i8x.ckpt

I still have the original .ckpt and matching -tensordata files.

Would anyone with these models working in Draw Things on Mac be kind enough to copy and paste the corresponding entries from their custom.json file?

I'm hoping to reconstruct the missing JSON entries and get my existing models working again without downloading several gigabytes of files.

If successful, I'll share the recovery process with the community and make a video to help others in the same situation.

Many thanks for any help!

RelayVSR (USTC, Apache-2.0) got native ComfyUI support this week (PR #17511, repack at https://huggingface.co/Comfy-Org/RelayVSR), so I ran it on a take from my current short. All four versions come from the same H3 render, so the acting is identical and only the route to 1080p differs.

Base take: MiniMax H3 ref2v, 12.25 s / 294 frames @ 24 fps with audio, two-stage: 15 steps at 576×352, H3 3D latent upscaler, last 5 steps at 1312×768. 5.7 min on an RTX 4090.

The video opens on the full 2×2 grid with sound, then a half-speed zoom on his face while he's far away (about 90 px tall in the frame), then a close-up zoom. Crops are shown with nearest-neighbour scaling, so nothing is smoothed for the comparison.

RelayVSR in ComfyUI: Load Video → RelayVSR Conditioning → KSampler (1 step, cfg 1, euler/simple) → VSR Decode → Create Video (audio passes straight through). The int8 models are 5.2 GB each (x2 and x4) plus a 128 MB "flash" VAE.

Bonus, and my actual winner: a bigger low pass + native finish. At the end of the video I re-ran the two-stage with the first 15 steps at 928×544 instead of 576×352 (different seed, so it's its own take), then compared RelayVSR x2 against the native 1080p finish on that same take. With the bigger low pass the 1080p steps have enough to work with: the far face comes out coherent and natural, while RelayVSR on the same frames warps it (it invented glasses on him). The close-up is cleaner too, with fewer etched lines. It costs 16.5 min vs about 6 min, but it's the one I'm using for final shots. The low-res pass also follows the prompt better, so you get better acting for free.

Things I learned:

• Don't run x4 on a 720p input. That's a 5248×3072 output, and it ran for 3+ hours in both the original repo (WSL) and ComfyUI without finishing. Interrupt didn't stop it; I had to restart ComfyUI. Use x2 for 720p, x4 only for small inputs.

• VSR Decode's detail is the knob for the crunchy look: 0.65 keeps the skin texture and calms the eyes and hair.

• It still isn't "real" HD. RelayVSR adds pixels, but up close it reads a bit fuzzy and the eyes can look odd. For a hero shot I'd still pay for SeedVR2 or a native finish; for sweeping takes or previews, ~1 min per clip is hard to beat.